def test_random_function_ndim(self):
# Test that random_function helper function accepts argument ndim
rng_R = random_state_type()
# ndim is an optional argument indicating the length of the 'shape'
# ndim not specified, OK
post_out4, out4 = uniform(rng_R, (4, ))
# ndim specified, consistent with shape, OK
post_out1_4, out1_4 = uniform(rng_R, (4, ), ndim=1)
post_out2_4_4, out2_4_4 = uniform(rng_R, (4, 4), ndim=2)
# ndim specified, but not compatible with shape
with pytest.raises(ValueError):
uniform(rng_R, (4, ), ndim=2)
f_ok = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_out2_4_4,
mutable=True,
)
],
[out4, out1_4, out2_4_4],
accept_inplace=True,
)
# The correct cases should execute properly
o4, o1_4, o2_4_4 = f_ok()
# Check the sanity of the answers
assert np.allclose(o4, o1_4)
assert np.allclose(o4, o2_4_4[0])
def test_vector_arguments(self):
rng_R = random_state_type()
low = tensor.vector()
post_r, out = uniform(rng_R, low=low, high=1)
assert out.ndim == 1
f = compile.function([rng_R, low], [post_r, out], accept_inplace=True)
def as_floatX(thing):
return np.asarray(thing, dtype=aesara.config.floatX)
rng_state0 = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
post0, val0 = f(rng_state0, [-5, 0.5, 0, 1])
post1, val1 = f(post0, as_floatX([0.9]))
numpy_val0 = as_floatX(numpy_rng.uniform(low=[-5, 0.5, 0, 1], high=1))
numpy_val1 = as_floatX(numpy_rng.uniform(low=as_floatX([0.9]), high=1))
assert np.all(val0 == numpy_val0)
assert np.all(val1 == numpy_val1)
high = tensor.vector()
post_rb, outb = uniform(rng_R, low=low, high=high)
assert outb.ndim == 1
fb = compile.function([rng_R, low, high], [post_rb, outb],
accept_inplace=True)
post0b, val0b = fb(post1, [-4.0, -2], [-1, 0])
post1b, val1b = fb(post0b, [-4.0], [-1])
numpy_val0b = as_floatX(numpy_rng.uniform(low=[-4.0, -2], high=[-1,
0]))
numpy_val1b = as_floatX(numpy_rng.uniform(low=[-4.0], high=[-1]))
assert np.all(val0b == numpy_val0b)
assert np.all(val1b == numpy_val1b)
with pytest.raises(ValueError):
fb(post1b, [-4.0, -2], [-1, 0, 1])
# TODO: do we want that?
# with pytest.raises(ValueError):
# fb(post1b, [-4., -2], [-1])
size = tensor.lvector()
post_rc, outc = uniform(rng_R, low=low, high=high, size=size, ndim=1)
fc = compile.function([rng_R, low, high, size], [post_rc, outc],
accept_inplace=True)
post0c, val0c = fc(post1b, [-4.0, -2], [-1, 0], [2])
post1c, val1c = fc(post0c, [-4.0], [-1], [1])
numpy_val0c = as_floatX(numpy_rng.uniform(low=[-4.0, -2], high=[-1,
0]))
numpy_val1c = as_floatX(numpy_rng.uniform(low=[-4.0], high=[-1]))
assert np.all(val0c == numpy_val0c)
assert np.all(val1c == numpy_val1c)
with pytest.raises(ValueError):
fc(post1c, [-4.0, -2], [-1, 0], [1, 2])
with pytest.raises(ValueError):
fc(post1c, [-4.0, -2], [-1, 0], [2, 1])
with pytest.raises(ValueError):
fc(post1c, [-4.0, -2], [-1, 0], [1])
with pytest.raises(ValueError):
fc(post1c, [-4.0, -2], [-1], [1])
def test_mixed_shape(self):
# Test when the provided shape is a tuple of ints and scalar vars
rng_R = random_state_type()
shape0 = tensor.lscalar()
shape = (shape0, 3)
post_r, u = uniform(rng_R, size=shape, ndim=2)
f = compile.function([rng_R, shape0], u)
rng_state0 = np.random.RandomState(utt.fetch_seed())
assert f(rng_state0, 2).shape == (2, 3)
assert f(rng_state0, 8).shape == (8, 3)
post_r, v = uniform(rng_R, size=shape)
g = compile.function([rng_R, shape0], v)
assert g(rng_state0, 2).shape == (2, 3)
assert g(rng_state0, 8).shape == (8, 3)
def test_uniform(self):
# Test that raw_random.uniform generates the same results as numpy.
# Check over two calls to see if the random state is correctly updated.
rng_R = random_state_type()
# Use non-default parameters
post_r, out = uniform(rng_R, (4, ), -2.0, 2.0)
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r,
mutable=True,
)
],
[out],
accept_inplace=True,
)
numpy_rng = np.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
numpy_val0 = numpy_rng.uniform(-2.0, 2.0, size=(4, ))
numpy_val1 = numpy_rng.uniform(-2.0, 2.0, size=(4, ))
assert np.allclose(val0, numpy_val0)
assert np.allclose(val1, numpy_val1)
def test_random_function_noshape_args(self):
# Test if random_function helper works with args but without shape
rng_R = random_state_type()
# No shape, default args -> OK
post_out, out = uniform(rng_R, size=None, ndim=2)
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_out,
mutable=True,
)
],
[out],
accept_inplace=True,
)
(o, ) = f()
# No shape, args that have to be broadcasted -> OK
low = tensor.TensorType(dtype="float64",
broadcastable=(False, True, True))()
high = tensor.TensorType(dtype="float64",
broadcastable=(True, True, True, False))()
post_out2, out2 = uniform(rng_R, size=None, ndim=2, low=low, high=high)
assert out2.ndim == 4
assert out2.broadcastable == (True, False, True, False)
g = compile.function(
[
low,
high,
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_out2,
mutable=True,
),
],
[out2],
accept_inplace=True,
)
low_v = [[[3]], [[4]], [[-5]]]
high_v = [[[[5, 8]]]]
(o2, ) = g(low_v, high_v)
assert o2.shape == (1, 3, 1, 2)
def test_dtype_normal_uniform_687(self):
# Regression test for #687.
rng_R = random_state_type()
assert (uniform(rng_R,
low=tensor.constant(0, dtype="float64"),
dtype="float32")[1].dtype == "float32")
assert (normal(rng_R,
avg=tensor.constant(0, dtype="float64"),
dtype="float32")[1].dtype == "float32")
def test_uniform_vector(self):
rng_R = random_state_type()
low = tensor.vector()
high = tensor.vector()
post_r, out = uniform(rng_R, low=low, high=high)
assert out.ndim == 1
f = compile.function([rng_R, low, high], [post_r, out],
accept_inplace=True)
def as_floatX(thing):
return np.asarray(thing, dtype=aesara.config.floatX)
low_val = as_floatX([0.1, 0.2, 0.3])
high_val = as_floatX([1.1, 2.2, 3.3])
rng = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
# Arguments of size (3,)
rng0, val0 = f(rng, low_val, high_val)
numpy_val0 = as_floatX(numpy_rng.uniform(low=low_val, high=high_val))
assert np.all(val0 == numpy_val0)
# arguments of size (2,)
rng1, val1 = f(rng0, low_val[:-1], high_val[:-1])
numpy_val1 = as_floatX(
numpy_rng.uniform(low=low_val[:-1], high=high_val[:-1]))
assert np.all(val1 == numpy_val1)
# Specifying the size explicitly
g = compile.function(
[rng_R, low, high],
uniform(rng_R, low=low, high=high, size=(3, )),
accept_inplace=True,
)
rng2, val2 = g(rng1, low_val, high_val)
numpy_val2 = as_floatX(
numpy_rng.uniform(low=low_val, high=high_val, size=(3, )))
assert np.all(val2 == numpy_val2)
with pytest.raises(ValueError):
g(rng2, low_val[:-1], high_val[:-1])
def test_symbolic_shape(self):
rng_R = random_state_type()
shape = tensor.lvector()
post_r, out = uniform(rng_R, shape, ndim=2)
f = compile.function([rng_R, shape], out)
rng_state0 = np.random.RandomState(utt.fetch_seed())
assert f(rng_state0, [2, 3]).shape == (2, 3)
assert f(rng_state0, [4, 8]).shape == (4, 8)
with pytest.raises(ValueError):
f(rng_state0, [4])
with pytest.raises(ValueError):
f(rng_state0, [4, 3, 4, 5])
def test_broadcast_arguments(self):
rng_R = random_state_type()
low = tensor.dvector()
high = tensor.dcol()
post_r, out = uniform(rng_R, low=low, high=high)
assert out.ndim == 2
f = compile.function([rng_R, low, high], [post_r, out],
accept_inplace=True)
rng_state0 = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
post0, val0 = f(rng_state0, [-5, 0.5, 0, 1], [[1.0]])
post1, val1 = f(post0, [0.9], [[1.0], [1.1], [1.5]])
post2, val2 = f(post1, [-5, 0.5, 0, 1], [[1.0], [1.1], [1.5]])
numpy_val0 = numpy_rng.uniform(low=[-5, 0.5, 0, 1], high=[1.0])
numpy_val1 = numpy_rng.uniform(low=[0.9], high=[[1.0], [1.1], [1.5]])
numpy_val2 = numpy_rng.uniform(low=[-5, 0.5, 0, 1],
high=[[1.0], [1.1], [1.5]])
assert np.all(val0 == numpy_val0), (val0, numpy_val0)
assert np.all(val1 == numpy_val1)
assert np.all(val2 == numpy_val2)
def test_default_shape(self):
rng_R = random_state_type()
post_r, out = uniform(rng_R)
f = compile.function([rng_R], [post_r, out], accept_inplace=True)
rng_state0 = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
post0, val0 = f(rng_state0)
post1, val1 = f(post0)
numpy_val0 = np.asarray(numpy_rng.uniform(),
dtype=aesara.config.floatX)
numpy_val1 = np.asarray(numpy_rng.uniform(),
dtype=aesara.config.floatX)
assert np.all(val0 == numpy_val0)
assert np.all(val1 == numpy_val1)
post_r, out = multinomial(rng_R)
g = compile.function([rng_R], [post_r, out], accept_inplace=True)
post2, val2 = g(post1)
numpy_val2 = np.asarray(numpy_rng.multinomial(n=1, pvals=[0.5, 0.5]),
dtype=aesara.config.floatX)
assert np.all(val2 == numpy_val2)
def test_infer_shape(self):
rng_R = random_state_type()
rng_R_val = np.random.RandomState(utt.fetch_seed())
# no shape specified, default args
post_r, out = uniform(rng_R)
self._compile_and_check([rng_R], [out], [rng_R_val], RandomFunction)
post_r, out = uniform(rng_R, size=None, ndim=2)
self._compile_and_check([rng_R], [out], [rng_R_val], RandomFunction)
"""
#infer_shape don't work for multinomial.
#The parameter ndim_added is set to 1 and in this case, the infer_shape
#inplementation don't know how to infer the shape
post_r, out = multinomial(rng_R)
self._compile_and_check([rng_R], [out], [rng_R_val],
RandomFunction)
"""
# no shape specified, args have to be broadcasted
low = tensor.TensorType(dtype="float64",
broadcastable=(False, True, True))()
high = tensor.TensorType(dtype="float64",
broadcastable=(True, True, True, False))()
post_r, out = uniform(rng_R, size=None, ndim=2, low=low, high=high)
low_val = [[[3]], [[4]], [[-5]]]
high_val = [[[[5, 8]]]]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
# multinomial, specified shape
"""
#infer_shape don't work for multinomial
n = iscalar()
pvals = dvector()
size_val = (7, 3)
n_val = 6
pvals_val = [0.2] * 5
post_r, out = multinomial(rng_R, size=size_val, n=n, pvals=pvals,
ndim=2)
self._compile_and_check([rng_R, n, pvals], [out],
[rng_R_val, n_val, pvals_val],
RandomFunction)
"""
# uniform vector low and high
low = dvector()
high = dvector()
post_r, out = uniform(rng_R, low=low, high=1)
low_val = [-5, 0.5, 0, 1]
self._compile_and_check([rng_R, low], [out], [rng_R_val, low_val],
RandomFunction)
low_val = [0.9]
self._compile_and_check([rng_R, low], [out], [rng_R_val, low_val],
RandomFunction)
post_r, out = uniform(rng_R, low=low, high=high)
low_val = [-4.0, -2]
high_val = [-1, 0]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
low_val = [-4.0]
high_val = [-1]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
# uniform broadcasting low and high
low = dvector()
high = dcol()
post_r, out = uniform(rng_R, low=low, high=high)
low_val = [-5, 0.5, 0, 1]
high_val = [[1.0]]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
low_val = [0.9]
high_val = [[1.0], [1.1], [1.5]]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
low_val = [-5, 0.5, 0, 1]
high_val = [[1.0], [1.1], [1.5]]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
# uniform with vector slice
low = dvector()
high = dvector()
post_r, out = uniform(rng_R, low=low, high=high)
low_val = [0.1, 0.2, 0.3]
high_val = [1.1, 2.2, 3.3]
size_val = (3, )
self._compile_and_check(
[rng_R, low, high],
[out],
[rng_R_val, low_val[:-1], high_val[:-1]],
RandomFunction,
)
# uniform with explicit size and size implicit in parameters
# NOTE 1: Would it be desirable that size could also be supplied
# as a Aesara variable?
post_r, out = uniform(rng_R, size=size_val, low=low, high=high)
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
# binomial with vector slice
n = ivector()
prob = dvector()
post_r, out = binomial(rng_R, n=n, p=prob)
n_val = [1, 2, 3]
prob_val = [0.1, 0.2, 0.3]
size_val = (3, )
self._compile_and_check(
[rng_R, n, prob],
[out],
[rng_R_val, n_val[:-1], prob_val[:-1]],
RandomFunction,
)
# binomial with explicit size and size implicit in parameters
# cf. NOTE 1
post_r, out = binomial(rng_R, n=n, p=prob, size=size_val)
self._compile_and_check([rng_R, n, prob], [out],
[rng_R_val, n_val, prob_val], RandomFunction)
# normal with vector slice
avg = dvector()
std = dvector()
post_r, out = normal(rng_R, avg=avg, std=std)
avg_val = [1, 2, 3]
std_val = [0.1, 0.2, 0.3]
size_val = (3, )
self._compile_and_check(
[rng_R, avg, std],
[out],
[rng_R_val, avg_val[:-1], std_val[:-1]],
RandomFunction,
)
# normal with explicit size and size implicit in parameters
# cf. NOTE 1
post_r, out = normal(rng_R, avg=avg, std=std, size=size_val)
self._compile_and_check([rng_R, avg, std], [out],
[rng_R_val, avg_val, std_val], RandomFunction)
# multinomial with tensor-3 probabilities
"""